To provide women academics and anyone with an interest in AI with the recognition they deserve, TechCrunch is initiating a series of interviews with exceptional women who have made significant contributions to the AI revolution. As the AI boom continues, we plan to publish several stories throughout the year that highlight important work that is frequently overlooked. View further profiles by clicking this link.
Please email us as readers if you think a name should be on the list that we’ve overlooked and we’ll try to add it. The following figures are important to be aware of:
The disparity between genders in AI
The Gray Lady explained how the present AI boom came to be in a late-year New York Times article, naming several of the typical suspects, including Sam Altman, Elon Musk, and Larry Page. The absence of women from the report caused it to go viral, rather than the content that was covered.
Twelve men made up The Times’ list, the majority of whom were executives in AI or tech firms. Many lacked any kind of formal AI education or training.
Unlike what the Times suggests, Musk and Page were not seated next to one other at a mansion in the Bay when the AI craze began. It started much earlier, with researchers, policymakers, ethicists, and enthusiasts putting in many hours of work in relative obscurity to lay the groundwork for the current generation of AI and GenAI systems.
One of the earliest AI textbooks was published in 1983 by retired computer scientist Elaine Rich, who was formerly at the University of Texas in Austin. In 1988, she became the director of a corporate AI lab. Decades ago, Cynthia Dwork, a professor at Harvard, made significant advances in distributed computing, differential privacy, and AI fairness. Additionally, in the late 1990s and early 2000s, roboticist Cynthia Breazeal, an MIT professor, co-founded the robotics business Jibo, and worked on creating Kismet, one of the first “social robots.”
Even while women have contributed greatly to the advancement of AI technology, their share in the global AI workforce is still quite small. A Stanford research from 2021 found that the proportion of women in tenure-track AI academics is only 16%. The co-authors of a different survey published by the World Economic Forum that same year discover that women only occupy 26% of AI and analytics-related jobs.
Worse news: rather than closing, the gender gap in AI is growing.
According to a 2019 analysis by Nesta, the U.K.’s innovation agency for social good, the percentage of academic AI articles co-authored by at least one woman hasn’t increased since the 1990s. Women produced or co-authored just 13.8% of the AI research articles on Arxiv.org, a preprint scientific paper repository, as of 2019, a steady decline over the previous ten years.
Causes of the discrepancy
There are numerous causes for the discrepancy. However, some of the more well-known (and evident) ones are highlighted in a Deloitte survey of women working in AI. These include prejudice due to not fitting into pre-existing male-dominated stereotypes in the field and criticism from male peers.
It begins in college: according to a Deloitte poll, 78% of female respondents stated they were unable to secure an internship in artificial intelligence or machine learning while still an undergraduate. Because of the disparate treatment of men and women, more than half (58%) of the respondents stated they ultimately quit at least one job. Additionally, 73% of the respondents thought about quitting the tech sector entirely as a result of the low compensation and limited opportunities for professional advancement.
The AI sector is being harmed by the absence of women.
According to Nesta’s analysis, women are more likely than men to take into account the societal, ethical, and political implications of their work with artificial intelligence. This is not surprising given that women live in a world where they face discrimination based solely on their gender, that the majority of products on the market are made for men, and that women with children are frequently expected to juggle work and their role as primary caregivers.
Hopefully, TechCrunch’s modest contribution—a series featuring successful women in AI—will contribute to a positive change. Yet obviously, there is still a lot of work to be done.
The women we feature have a lot to offer anyone looking to advance and improve the field of artificial intelligence. However, a consistent theme emerges: robust guidance, dedication, and setting a good example. Organizations can effect change by implementing policies that support women entering or aspiring to enter the AI business, whether in the areas of hiring, education, or other areas. Furthermore, those in positions of authority can use their influence to promote more varied and encouraging work environments for women.
It won’t happen suddenly for change. However, every revolution starts with a tiny step.
